Abstract
BackgroundObesity is increasing globally. Chronic kidney disease (CKD) is strongly associated with obesity. Kidney function is commonly estimated with equations using creatinine (such as CKD-EPI equation) which is a product of muscle metabolism. Decisions about categorizing CKD, planning modality of renal replacement therapies, and adjusting dosages of medications excreted by the kidneys are done using these equations. However, it is not well appreciated that creatinine-based equations may not accurately estimate kidney function in obese individuals. We plan a systematic review of diagnostic studies which will compare estimating equations to actual measured kidney function.MethodsWe will systematically search electronic bibliographic databases including MEDLINE, EMBASE, and the Cochrane Library with no restrictions on language or specific dates. The search terms will be adapted for the different databases using a combination of Medical Subject Heading and relevant keywords contained in titles and abstracts. Our preliminary search strategy using Cochrane, MEDLINE, and EMBASE databases have identified 190, 1246, and 1660 citations, respectively. For all studies selected, we will extract information on general study characteristics, study participant (age, sex, ethnicity, weight, height, BMI, BSA), type and protocol of reference standard utilized, the index test studied, the methodology of measurement of index test, categories of GFR, the proportion of eGFR within 10, 20, 30, 40, and 50% of measured GFR, and bias between eGFR and measured GFR. If the quality of methods and risk of bias are adequate, we will perform a meta-analysis. We will assess the heterogeneity using the χ2 and the I2 statistics to examine whether the estimates from studies included could be pooled. Sensitivity and multivariate meta-regression analyses will be performed to assess the effects of clinical factors and socio-demographic characteristics reported in included studies on the meta-analytic estimates. All analysis will be performed using the Comprehensive Meta-analysis software.DiscussionThis systematic review might help to inform clinicians on the best equation to use in patients with obesity and CKD for staging of CKD and for medication dosing. If no equation is deemed suitable, this review will form a basis for future studies of GFR in obese individuals.Systematic review registrationPROSPERO CRD42018104345
Highlights
To facilitate prognostication and management of Chronic kidney disease (CKD), it is classified into 6 glomerular filtration rate (GFR) categories [GFR category 1: GFR ≥ 90 ml/min/1.73 m2, GFR category 2: GFR ≥ 60 and < 90 ml/min/1.73 m2, GFR category 3a: GFR 45 to < 60 ml/min/1.73 m2, GFR category: GFR ≥ 30 and < 45 ml/min/1.73 m2, GFR category 4: GFR ≥ 15 and < 30 ml/min/1.73 m2, and GFR category 5: GFR < 15 ml/min/1.73 m2] [13].Many commonly used medications are excreted by the kidneys and need dose adjustment which is based on GFR [14]
The creatinine-based Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equation calculated estimated glomerular filtration rate (eGFR) is reported by many laboratories automatically, whenever serum creatinine is requested
The CKD-EPI equation was derived from 5504 individuals with a mean body mass index (BMI) of 28 (SD 6) kg/m2, with only 29% of patients having diabetes, and was validated in 3875 patients with a mean BMI of 27 (SD 6) kg/m2) [29]
Summary
We will systematically search electronic bibliographic databases including MEDLINE, EMBASE, and the Cochrane Library with no restrictions on language or specific dates. The search terms will be adapted for the different databases using a combination of Medical Subject Heading and relevant keywords contained in titles and abstracts. Our preliminary search strategy using Cochrane, MEDLINE, and EMBASE databases have identified 190, 1246, and 1660 citations, respectively. If the quality of methods and risk of bias are adequate, we will perform a meta-analysis. We will assess the heterogeneity using the χ2 and the I2 statistics to examine whether the estimates from studies included could be pooled. Sensitivity and multivariate meta-regression analyses will be performed to assess the effects of clinical factors and socio-demographic characteristics reported in included studies on the meta-analytic estimates. All analysis will be performed using the Comprehensive Meta-analysis software
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